Kirjahaku

India's over 200 million Dalits, once called "untouchables," have been mobilized by social movements and political parties, but the outcomes of this mobilization are puzzling. Dalits' ethnic parties have performed poorly in elections in states where movements demanding social equality have been strong while they have succeeded in states where such movements have been entirely absent or weak. In Mobilizing the Marginalized, Amit Ahuja demonstrates that the collective action of marginalized groups--those that are historically stigmatized and disproportionately poor — is distinct. Drawing on extensive original research conducted across four of India's largest states, he shows, for the marginalized, social mobilization undermines the bloc voting their ethnic parties' rely on for electoral triumph and increases multi-ethnic political parties' competition for marginalized votes. He presents evidence showing that a marginalized group gains more from participating in a social movement and dividing support among parties than from voting as a bloc for an ethnic party.

India's over 200 million Dalits, once called "untouchables," have been mobilized by social movements and political parties, but the outcomes of this mobilization are puzzling. Dalits' ethnic parties have performed poorly in elections in states where movements demanding social equality have been strong while they have succeeded in states where such movements have been entirely absent or weak. In Mobilizing the Marginalized, Amit Ahuja demonstrates that the collective action of marginalized groups--those that are historically stigmatized and disproportionately poor — is distinct. Drawing on extensive original research conducted across four of India's largest states, he shows, for the marginalized, social mobilization undermines the bloc voting their ethnic parties' rely on for electoral triumph and increases multi-ethnic political parties' competition for marginalized votes. He presents evidence showing that a marginalized group gains more from participating in a social movement and dividing support among parties than from voting as a bloc for an ethnic party.

Revision with unchanged content. A data stream being transmitted over a network channel with capacity less than the data transmission rate of the data stream causes sequential network problems. In this book, we present a new approach for shedding less-informative attribute data from a data stream to maintain the data trans-mission rate less than the network channel capacity. A scheme for shedding attributes, instead of tuples, becomes imperative in stream data, since shedding a complete tuple would lead to shedding some informative, as well as less-informative, attribute data in the tuple. Since data shed at the source site may be of interest to the user at the destination site, we design a data recovery approach, which maintains the minimal amount of information for data recovery purpose while imposing minimal overhead for data recovery on the source site. Our load shedding and data recovery approach (i) handles wide range of data streams in different application domains, (ii) is dynamic in nature, since each load shedding scheme adjusts the amount of data to be shed according to the current load and network capacity, and (iii) is adoptive, which is appealing in an ever-changing network environment, and (iv) is not based on queries, but works on general data streams instead.